This work contains two contributions. First, we present a simple and robust procedure for triangulation of freely moving rodents from multiple cameras that are orthogonal towards a camera positioned underneath the plane of movement (Figure REF ).
Second, we evaluate two model architectures that have been proposed for 3D pose lifting of human poses previously, one a linear residual network [1]} and the other a dilated temporal convolutional residual network [2]}, aiming to establish which of both models works better for our triangulated rodent pose data.
Moreover, we evaluate the choice of the temporal window setting on the performance of the temporal convolutional model as well as the choice of the viewing perspective on the performance of both models.
The aim of this work is to pave the way for robust detection of 3D rodent poses from single poses, allowing for studies of behavior in complex laboratory as well as naturalistic environments.
